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| enum | DynamicModel { RandomWalk,
AutoRegression1
} |
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| enum | UpdateType { Additive,
Compositional
} |
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| enum | ResamplingType { None,
BinaryMultinomial,
LinearMultinomial,
Residual
} |
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| enum | LikelihoodFunc { AM,
Gaussian,
Reciprocal
} |
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| enum | MeanType { None,
SSM,
Corners
} |
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| PFParams (int _max_iters, int _n_particles, double _epsilon, DynamicModel _dyn_model, UpdateType _upd_type, LikelihoodFunc _likelihood_func, ResamplingType _resampling_type, MeanType _mean_type, bool _reset_to_mean, const vectorvd &_ssm_sigma, const vectorvd &_ssm_mean, bool _update_distr_wts, double _min_distr_wt, double _adaptive_resampling_thresh, const vectord &_pix_sigma, double _measurement_sigma, int _show_particles, bool _enable_learning, bool _jacobian_as_sigma, bool _debug_mode) |
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| PFParams (const PFParams *params=nullptr) |
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bool | processDistributions (vector< VectorXd > &state_sigma, vector< VectorXd > &state_mean, VectorXi &distr_n_samples, unsigned int &n_distr, unsigned int ssm_state_size) |
| | parse the provided mean and sigma and apply several priors to get the final parameters for all distributions
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static const char * | toString (DynamicModel _dyn_model) |
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static const char * | toString (UpdateType _upd_type) |
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static const char * | toString (ResamplingType _resampling_type) |
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static const char * | toString (LikelihoodFunc _likelihood_func) |
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static const char * | toString (MeanType _likelihood_func) |
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int | max_iters |
| | maximum iterations of the PF algorithm to run for each frame
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int | n_particles |
| | number of particles to use
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double | epsilon |
| | iterations will be terminated when L2 norm of the change in tracker corners exceeds this
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DynamicModel | dynamic_model |
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UpdateType | update_type |
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LikelihoodFunc | likelihood_func |
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ResamplingType | resampling_type |
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| MeanType | mean_type |
| | method used for computing the mean of the SSM states corresponding to the particles. More...
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bool | reset_to_mean |
| | reset all particles to the mean/optimal corners found in each frame
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vectorvd | ssm_sigma |
| | standarsd deviations of the Gaussian distributions to use for the samplers
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vectorvd | ssm_mean |
| | mean of the Gaussian distributions to use for the samplers
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bool | update_distr_wts |
| | update the proportion of samples taken from different sampler according to the weights of the samples generated by each
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double | min_distr_wt |
| | fraction of the total particles that will always be evenly distributed between the samplers;
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double | adaptive_resampling_thresh |
| | maximum ratio between the number of effective particles and the total particles for resampling to be performed; setting it to <=0 or >1 disables adaptive resampling
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vectord | pix_sigma |
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double | measurement_sigma |
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int | show_particles |
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bool | enable_learning |
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bool | jacobian_as_sigma |
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bool | debug_mode |
| | decides whether logging data will be printed for debugging purposes;
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| MeanType PFParams::mean_type |
method used for computing the mean of the SSM states corresponding to the particles.
\ 0: No mean computed - just use the state of the particle with the highest weight 1: let the SSM compute the mean of the samples 2: mean of the corners of the bounding boxes corresponding to the particles
The documentation for this struct was generated from the following file:
1.8.11
